如何用python netCDF4创建netCDF文件？

Question

我正在学习如何使用Anaconda中的Pyhton模块来使用netCDF4。我正在尝试将值附加到我创建的time和field的两个变量中

from netCDF4 import Dataset
import numpy as np

root_grp = Dataset('py_netcdf4.nc', 'w', format='NETCDF4')
root_grp.description = 'Example simulation data'

ndim = 128 # Size of the matrix ndim*ndim
xdimension = 0.75
ydimension = 0.75
# dimensions
root_grp.createDimension('time', None)
root_grp.createDimension('x', ndim)
root_grp.createDimension('y', ndim)

# variables
time = root_grp.createVariable('time', 'f8', ('time',))
x = root_grp.createVariable('x', 'f4', ('x',))
y = root_grp.createVariable('y', 'f4', ('y',))
field = root_grp.createVariable('field', 'f8', ('time', 'x', 'y',))

# data
x_range =  np.linspace(0, xdimension, ndim)
y_range =  np.linspace(0, ydimension, ndim)
x[:] = x_range
y[:] = y_range
for i in range(5):
    time[i] = i*50.0
    field[i,:,:] = np.random.uniform(size=(len(x_range), len(y_range)))


root_grp.close

但是现在当我打印其中一个变量时，我得到的结果是它是空的(！！)：

Python 2.7.10 |Anaconda 2.4.1 (64-bit)| (default, Sep 15 2015, 14:50:01) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> from netCDF4 import Dataset
>>> root_grp = Dataset('py_netcdf4.nc', 'r', format='NETCDF4')
>>> print root_grp.variables["field"][:,:,:]
[]
>>> 

我在这里做错了什么？

Answer 1

这是可行的：

from netCDF4 import Dataset
import numpy as np

root_grp = Dataset('py_netcdf4.nc', 'w', format='NETCDF4')
root_grp.description = 'Example simulation data'

ndim = 128 # Size of the matrix ndim*ndim
xdimension = 0.75
ydimension = 0.75
# dimensions
root_grp.createDimension('time', None)
root_grp.createDimension('x', ndim)
root_grp.createDimension('y', ndim)

# variables
time = root_grp.createVariable('time', 'f8', ('time',))
x = root_grp.createVariable('x', 'f4', ('x',))
y = root_grp.createVariable('y', 'f4', ('y',))
field = root_grp.createVariable('field', 'f8', ('time', 'x', 'y',))

# data
x_range =  np.linspace(0, xdimension, ndim)
y_range =  np.linspace(0, ydimension, ndim)
x[:] = x_range
y[:] = y_range
for i in range(5):
    time[i] = i*50.0
    field[i,:,:] = np.random.uniform(size=(len(x_range), len(y_range)))


root_grp.close()

唯一的区别是我调用了close()方法：root_grp.close()。